WO2014043353A2 - Methods, devices and systems for detecting objects in a video - Google Patents

Methods, devices and systems for detecting objects in a video Download PDF

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Publication number
WO2014043353A2
WO2014043353A2 PCT/US2013/059471 US2013059471W WO2014043353A2 WO 2014043353 A2 WO2014043353 A2 WO 2014043353A2 US 2013059471 W US2013059471 W US 2013059471W WO 2014043353 A2 WO2014043353 A2 WO 2014043353A2
Authority
WO
WIPO (PCT)
Prior art keywords
human
locations
image
foreground
video
Prior art date
Application number
PCT/US2013/059471
Other languages
English (en)
French (fr)
Other versions
WO2014043353A3 (en
Inventor
Zhong Zhang
Weihong Yin
Peter Venetianer
Original Assignee
Objectvideo, Inc.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority to BR112015005282-7A priority Critical patent/BR112015005282B1/pt
Priority to SG11201501725RA priority patent/SG11201501725RA/en
Priority to KR1020157009258A priority patent/KR102358813B1/ko
Priority to EP13837037.4A priority patent/EP2895986B1/en
Priority to CA2884383A priority patent/CA2884383C/en
Priority to RU2015109072A priority patent/RU2635066C2/ru
Priority to MX2015003153A priority patent/MX347511B/es
Priority to JP2015532044A priority patent/JP6424163B2/ja
Application filed by Objectvideo, Inc. filed Critical Objectvideo, Inc.
Priority to CN201380047668.5A priority patent/CN104813339B/zh
Priority to AU2013315491A priority patent/AU2013315491B2/en
Publication of WO2014043353A2 publication Critical patent/WO2014043353A2/en
Publication of WO2014043353A3 publication Critical patent/WO2014043353A3/en
Priority to IL237647A priority patent/IL237647B/en
Priority to SA515360136A priority patent/SA515360136B1/ar
Priority to ZA2015/02413A priority patent/ZA201502413B/en

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/103—Static body considered as a whole, e.g. static pedestrian or occupant recognition
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/70—Determining position or orientation of objects or cameras
    • G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
    • G06T7/75—Determining position or orientation of objects or cameras using feature-based methods involving models
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
    • G06V20/53—Recognition of crowd images, e.g. recognition of crowd congestion
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
    • G06V20/54—Surveillance or monitoring of activities, e.g. for recognising suspicious objects of traffic, e.g. cars on the road, trains or boats
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10016—Video; Image sequence
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20076—Probabilistic image processing
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30196—Human being; Person
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30232—Surveillance

Definitions

  • the disclosed embodiments provide methods, devices and systems for intelligent analysis of video images to detect objects, such as human objects.
  • Figure 3 A illustrates an exemplary flow diagram for target detection and counting according to an exemplary embodiment of the invention.
  • Software may refer to prescribed rules to operate a computer. Examples of software may include: software; code segments; instructions; applets; pre-compiled code; compiled code; interpreted code; computer programs; and programmed logic.
  • 7,932,923 are also incorporated by reference for exemplary details of video primitive (or metadata) generation and downstream processing (which may be real time processing or later processing) to obtain information from the video, such as event detection, using the generated video primitives, which may be used with the embodiments disclosed herein.
  • Each module 103- 108, as well as their individual components, alone or as combined with other modules/components, may be implemented by dedicated hardware (circuitry), software and/or firmware.
  • a general purpose computer programmed with software may implement all of the modules.
  • computer readable media containing software that may be used to configure a computer to perform the operations described herein comprise further embodiments of the invention.
  • Generic human model module 303 also provides an estimate of various sizes of the 2D human model at corresponding locations within the image space.
  • the image space may correspond to the two dimensional space of an image in a frame of video provided by video source 102.
  • An image space may be measured in pixel increments, such that locations within the image space are identified by pixel coordinates.
  • a video camera may take a video image, comprising a two-dimensional image of the three dimensional real world. When a human is present at a certain location within the real world, the human may be expected to occupy a certain amount of foreground at a certain location within the two dimensional video image.
  • the system may correlate the known height of the calibration model to a size in the 2D video image. For example, when a center of the calibration model is at location (xl, yl), the height of the calibration model may be 15 pixels (or may be measured in some other measurement). When the center of the calibration model is at location (x2, y2), the calibration model may be 27 pixels in height.
  • Human probability map computation module 305 uses the foreground blob set of a particular frame of a video image output by the foreground blob detection module 105 and the human models with their corresponding identifying coordinates output from the human based camera calibration model 304 to compute the human target probability for each of plural locations within the two dimensional video image, such as for each image pixel location.
  • the plural calculated probabilities may be associated with the plural locations to create a probability map.
  • the plural locations may be the same as the (x, y) identifying coordinates of the human models.
  • Figure 16 illustrates an exemplary method of updating the crowd gathering spots and detecting of crowd "gathering" and “disperse” events.
  • Block 1601 updates the location and area of the crowd gathering spot using the new human detection results on the video frame under consideration.
  • Block 1602 checks if the crowd "gathering" event has been detected from the current crowd gathering spot. If “no”, block 1603 continues to detect the "gathering” event by checking if a crowd gathering spot has been successfully updated for certain duration. This duration threshold may be set by the user at the rule definition time. Once a crowd gathering spot has generated a "gathering" event, block 1604 further monitor the gathering spot to detect the "disperse” event.

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Multimedia (AREA)
  • Human Computer Interaction (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Image Analysis (AREA)
  • Closed-Circuit Television Systems (AREA)
  • Burglar Alarm Systems (AREA)
PCT/US2013/059471 2012-09-12 2013-09-12 Methods, devices and systems for detecting objects in a video WO2014043353A2 (en)

Priority Applications (13)

Application Number Priority Date Filing Date Title
MX2015003153A MX347511B (es) 2012-09-12 2013-09-12 Metodos, dispositivos y sistemas para detectar objetos en un video.
KR1020157009258A KR102358813B1 (ko) 2012-09-12 2013-09-12 비디오 내의 객체들을 탐지하기 위한 방법들, 장치들 및 시스템들
EP13837037.4A EP2895986B1 (en) 2012-09-12 2013-09-12 Methods, devices and systems for detecting objects in a video
CA2884383A CA2884383C (en) 2012-09-12 2013-09-12 Methods, devices and systems for detecting objects in a video
RU2015109072A RU2635066C2 (ru) 2012-09-12 2013-09-12 Способ обнаружения человеческих объектов в видео (варианты)
JP2015532044A JP6424163B2 (ja) 2012-09-12 2013-09-12 ビデオ内の対象物を検出するための方法、装置及びシステム
CN201380047668.5A CN104813339B (zh) 2012-09-12 2013-09-12 用于检测视频中的对象的方法、设备和系统
BR112015005282-7A BR112015005282B1 (pt) 2012-09-12 2013-09-12 Métodos de detecção de indivíduos humanos em um vídeo
SG11201501725RA SG11201501725RA (en) 2012-09-12 2013-09-12 Methods, devices and systems for detecting objects in a video
AU2013315491A AU2013315491B2 (en) 2012-09-12 2013-09-12 Methods, devices and systems for detecting objects in a video
IL237647A IL237647B (en) 2012-09-12 2015-03-10 Methods, devices and systems for detecting objects in video
SA515360136A SA515360136B1 (ar) 2012-09-12 2015-03-12 طرق وأجهزة وأنظمة للكشف عن أهداف في محتوى فيديو
ZA2015/02413A ZA201502413B (en) 2012-09-12 2015-04-10 Methods, devices and systems for detecting objects in a video

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
US201261700033P 2012-09-12 2012-09-12
US61/700,033 2012-09-12
US13/838,511 US9165190B2 (en) 2012-09-12 2013-03-15 3D human pose and shape modeling
US13/838,511 2013-03-15

Publications (2)

Publication Number Publication Date
WO2014043353A2 true WO2014043353A2 (en) 2014-03-20
WO2014043353A3 WO2014043353A3 (en) 2014-06-26

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PCT/US2013/059471 WO2014043353A2 (en) 2012-09-12 2013-09-12 Methods, devices and systems for detecting objects in a video

Country Status (15)

Country Link
US (3) US9165190B2 (enrdf_load_stackoverflow)
EP (1) EP2895986B1 (enrdf_load_stackoverflow)
JP (1) JP6424163B2 (enrdf_load_stackoverflow)
KR (1) KR102358813B1 (enrdf_load_stackoverflow)
CN (2) CN107256377B (enrdf_load_stackoverflow)
AU (1) AU2013315491B2 (enrdf_load_stackoverflow)
CA (1) CA2884383C (enrdf_load_stackoverflow)
IL (1) IL237647B (enrdf_load_stackoverflow)
MX (1) MX347511B (enrdf_load_stackoverflow)
MY (1) MY175059A (enrdf_load_stackoverflow)
RU (1) RU2635066C2 (enrdf_load_stackoverflow)
SA (1) SA515360136B1 (enrdf_load_stackoverflow)
SG (1) SG11201501725RA (enrdf_load_stackoverflow)
WO (1) WO2014043353A2 (enrdf_load_stackoverflow)
ZA (1) ZA201502413B (enrdf_load_stackoverflow)

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